Abstract
The digital universe is expanding at very high rates. New ways of retrieving and enriching text and audio content are required. In this work, a methodology for actor level emotion magnitude prediction in text and speech is proposed. A model is trained to predict emotion magnitudes per actor at any point in a story using previous emotion magnitudes plus current text and speech features which act on the actor’s emotional state. The methodology compares linear and non-linear regression techniques to determine the optimal model that fits the data. Results of the analysis show that non-linear regression models based on Support Vector Regression (SVR) using a Radial Basis Function (RBF) kernel provide the most accurate prediction model. An analysis of the contribution of the features for emotion magnitude prediction is performed.
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Calix, R.A., Knapp, G.M. Actor level emotion magnitude prediction in text and speech. Multimed Tools Appl 62, 319–332 (2013). https://doi.org/10.1007/s11042-011-0909-8
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DOI: https://doi.org/10.1007/s11042-011-0909-8